Learning Sampling Distributions for Robot Motion Planning (1709.05448v3)

Abstract: A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to "small" regions of the state space, due to, for example, differential constraints or collision-avoidance constraints. To accelerate the planning process, it is thus desirable to devise non-uniform sampling strategies that favor sampling in those regions where an optimal solution might lie. This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling. The sampling distribution is computed through a conditional variational autoencoder, allowing sample generation from the latent space conditioned on the specific planning problem. This methodology is general, can be used in combination with any sampling-based planner, and can effectively exploit the underlying structure of a planning problem while maintaining the theoretical guarantees of sampling-based approaches. Specifically, on several planning problems, the proposed methodology is shown to effectively learn representations for the relevant regions of the state space, resulting in an order of magnitude improvement in terms of success rate and convergence to the optimal cost.

• The paper presents a CVAE method that learns and biases sampling towards regions likely to yield successful motion plans.
• It achieves nearly an order of magnitude improvement in success rates and reduced path costs compared to uniform sampling techniques.
• The approach integrates machine learning with traditional SBMP, offering scalable solutions and enhanced theoretical planning guarantees.

An Expert Overview of "Learning Sampling Distributions for Robot Motion Planning"

The paper "Learning Sampling Distributions for Robot Motion Planning" by Ichter, Harrison, and Pavone presents a methodology for improving sampling-based motion planning (SBMP) performance through the use of learned non-uniform sampling distributions. The authors propose leveraging a Conditional Variational Autoencoder (CVAE) to learn these distributions from demonstrations or historical data of successful plans, which are then used to bias the sampling process in new planning problems. This approach fundamentally shifts from traditional uniform sampling methods, allowing the algorithm to focus computational efforts on regions of the state space more likely to contain solutions, dictated by both system-specific and problem-specific constraints.

Methodology

The core of the methodology revolves around learning sampling distributions using CVAEs, which capture complex problem structures and dynamics constraints inherent in planning tasks. Training is conducted offline with successful trajectories and demonstration data that inform the distribution of feasible states in new sampling tasks. Once trained, the CVAE can generate samples conditioned on specific problem attributes, such as initial state, goal region, and workspace obstacles. This is a critical departure from uniform sampling, which lacks contextual adaptability and efficiency in high-dimensions or complex environments.

Performance Evaluation

The paper demonstrates the methodology's efficacy across various robotic systems, including geometric problems, spacecraft trajectory planning, multi-robot systems, and kinematic chains. Quantitatively, the approach yields significant improvements: nearly an order of magnitude better success rates and reduced path costs compared to traditional strategies. Notably, the learned distributions often achieve optimal or near-optimal solutions rapidly, contrasting starkly with the gradual convergence observed in uniform sampling methods.

Implications and Extensions

The implications for practical robotics are substantial. By tailoring the sample distribution, planners can overcome bottlenecks posed by constrained or complex environments, unlocking more efficient and scalable solutions for real-world tasks. The methodology is sufficiently general to accommodate diverse robots and planning scenarios, supporting its potential widespread applicability.

Theoretical implications also abound, as this integration of machine learning with traditional sampling algorithms could redefine SBMP's computational guarantees, such as completeness and asymptotic optimality. The concept of adaptive sample distribution during the planning phase indicates potential for further axes of research, such as exploration of adaptive learning schemes or hierarchical sample refinement.

Future Research Directions

The authors highlight several areas ripe for future exploration. For instance, refining the CVAE's model to better incorporate semantic workspace information could enhance its interpretability and utility in more abstract environments. Additionally, developing methods for learning structured, non-independent sample sets might further stretch the boundaries of SBMP efficiency through improved dispersion and coverage.

The paper successfully bridges a gap between machine learning and robotic motion planning, providing a framework that not only accelerates planning but also maintains theoretical robustness. This work thus represents a promising direction for leveraging learning in robotics, setting a strong foundation for further investigations into adaptive and intelligent motion planning systems.